Extraction Pattern Analysis Of Biological Information Feature Under Data Mining

With the increase of the amount of biological data, the most important problem in front of us is how to analyze and study such a large amount of biological data, and to clarify the biological significance of it. In this context, bioinformatics is bred and born. The study of bioinformatics is widely used, including the establishment and evaluation of biological information database, sequence alignment, protein alignment, gene recognition, drug design, and so on. In this paper, we use various data mining methods, and focus on the research of sequence alignment, data and quantitative analysis of TCM diagnosis information, and the verification of the model of association rules. We analyze the patterns of biological information feature extraction, and then to extract valuable information. Specific work is as follows:In chapter one, the contents of bioinformatics and relevant knowledge of data mining are simply introduced. In addition, the main work and innovation of this paper are given.In chapter two, we research on the Closest String Problem. For this combinatorial optimization problem, GRASP-CSP algorithm is adopted by using a probability-based heuristic. Due to the independence of iterative process, the narrow range of search scope and the single judgment indicator, we propose a new revised algorithm, namely IGRASP-CSP.In chapter three, using mathematical modeling to calculate fingerprint image, research on data extraction and quantitative analysis of TCM diagnosis information. The establishment of CGR graph of four kinds of common disease fingerprint sequence is proposed, and then the definition of relative degree of relative usage (QRSFU) of synonymous fingerprint sequences in four common diseases is given. Using SPSS and SAS, the correlation analysis of fingerprint graph has been done in this paper.In chapter four, using mining method of association rules to research results by reasons or trace reasons by results. Using tourism data to design model and verify. Obtain data through actual survey, and then causal association in the database is mined from the effective questionnaire. Finally, an effective computational model is established on the basis of statistical analysis method.In chapter five, the research work is summarized and prospect.